Abstract
The goal of this paper is to present a new algorithm that filters out inconsistent instances from the training dataset for further usage with machine learning algorithms or learning of neural networks. The idea of this algorithm is based on the previous state-of-the-art algorithm, which uses the concept of local sets. Sophisticated modification of the definition of local sets changes the merits of the algorithm. It is additionally supported by locality-sensitive hashing used for searching for nearest neighbors, composing a new efficient (\(O(n\log n)\)), and an accurate algorithm.
Results prepared on many benchmarks show that the algorithm is as accurate as previous but strongly reduces the time complexity.
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Jankowski, N. (2023). A Fast and Efficient Algorithm for Filtering the Training Dataset. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Lecture Notes in Computer Science, vol 13623. Springer, Cham. https://doi.org/10.1007/978-3-031-30105-6_42
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